Biological QA after resequencing of all the batches
Thomas Dobrenel and Nicolas Delhomme
2019-12-11
- 1 Setup
- 2 Raw data
- 3 Expression data
- 4 Data normalisation
- 5 Multivariate analysis
- 6 Differential expression based on the nutrition and treatment at T6
- 7 Differential expression based on the nutrition and treatment at T24
- 8 Comparisons of GOI lists (Venn diagrams)
- 9 Export list of DEGs
- 10 Session Info
1 Setup
- Set the working dir
setwd("/mnt/picea/projects/arabidopsis/jhanson/arabidopsis-nutrition-TOR/all-batches")- Load libraries
suppressPackageStartupMessages({
library(data.table)
library(DESeq2)
library(gplots)
library(here)
library(hyperSpec)
library(limma)
library(LSD)
library(magrittr)
library(matrixStats)
library(parallel)
library(pander)
library(plotly)
library(RColorBrewer)
library(scatterplot3d)
library(tidyverse)
library(tximport)
library(VennDiagram)
library(vsn)
})- Source some helper functions
suppressPackageStartupMessages({
source("~/Git/UPSCb/UPSCb-common/src/R/featureSelection.R")
source("~/Git/UPSCb/UPSCb-common/src/R/gopher.R")
source("~/Git/UPSCb/UPSCb-common/src/R/plot.multidensity.R")
source("~/Git/UPSCb/UPSCb-common/src/R/volcanoPlot.R")
})- Cutoff Different from Schurch et al., RNA, 2016 (LFC at 1 instead of 0,5)
lfc <- 1
FDR <- 0.01- Create palettes
pal <- c(brewer.pal(8,"Dark2"),1)
pal2 <- brewer.pal(9,"Paired") #require package RColorBrewer
cols <- rainbow(17)
hpal <- colorRampPalette(c("blue","white","red"))(100)- Register the default plot margin
mar <- par("mar")2 Raw data
2.1 Loading of sample information
- Read the sample information
samples <- read.csv("~/Git/UPSCb/projects/arabidopsis-nutrition-TOR/doc/samples3.csv")- Remove unnecessary samples
samples %<>% filter(!grepl("P11554_1",SciLifeID)) %>%
filter(! SciLifeID %in% c("P13406_101",
"P14066_128",
"P14066_133",
"P13406_102",
"P14066_131")) %>%
mutate(Nutrition,Nutrition=relevel(Nutrition,"NPS")) %>%
mutate(AZD,AZD=relevel(AZD,"DMSO"))
samples <- samples[order(samples$Timepoint, samples$Nutrition, samples$AZD),]
samples %<>% mutate(Timepoint,Timepoint=factor(paste0("T",Timepoint)))2.1.1 Load the data
- Call the data
filenames <- list.files("../Salmon",
recursive = TRUE,
pattern = "quant.sf",
full.names = TRUE)- Name the data
names(filenames) <- sub("_S.*","",sapply(strsplit(filenames, "/"), .subset, 3))- Match data <=> sample list
filenames <- filenames[match(samples$SciLifeID,names(filenames))]
filenames <-filenames[!is.na(names(filenames))]
samples <- samples[match(names(filenames),samples$SciLifeID),]- Annotate the samples
samples$Conditions <- factor(paste(samples$Timepoint,samples$Nutrition,samples$AZD,sep="_"))
samples$Batch <- factor(substr(samples$SciLifeID,1,8))3 Expression data
Read the expression at the transcript level
tx <- suppressMessages(tximport(files = filenames,
type = "salmon",
txOut = TRUE))summarise to genes
tx2gene <- data.frame(TXID=rownames(tx$counts),
GENEID=sub("\\.[0-9]+","",rownames(tx$counts)))
gx <- summarizeToGene(tx,tx2gene=tx2gene)## summarizing abundance## summarizing counts## summarizing lengthkg <- round(gx$counts) Sanity check
stopifnot(all(colnames(kg) == samples$SciLifeID))3.1 Raw data export
#dir.create(file.path("analysis_Tom","salmon"),showWarnings=FALSE,recursive=TRUE)
#write.table(kg,file="/mnt/picea/projects/arabidopsis/jhanson/arabidopsis-nutrition-TOR/analysis_Tom/nutrition-unormalised-gene-expression_data.csv")
#save(kg, samples, file = "/mnt/picea/projects/arabidopsis/jhanson/arabidopsis-nutrition-TOR/analysis_Tom/counts.rda")3.2 Preliminary validations
3.2.1 Check for the genes that are never expressed
sel <- rowSums(kg) == 0
sprintf("%s%% (%s) of %s genes are not expressed",
round(sum(sel) * 100/ nrow(kg),digits=1),
sum(sel),
nrow(kg))## [1] "16.9% (5452) of 32310 genes are not expressed"4 Data normalisation
For visualization, the data is submitted to a variance stabilization transformation using DESeq2. The dispersion is estimated independently of the sample type
dds <- DESeqDataSetFromMatrix(
countData = kg,
colData = samples,
design = ~ Conditions)## converting counts to integer modedds <- estimateSizeFactors(dds)4.1 Perform a Variance Stabilizing Transformation for plotting
vst <- varianceStabilizingTransformation(dds,blind=FALSE)
vsd <- assay(vst)
vsd <- vsd - min(vsd)Write out
#write.csv(vst,"/mnt/picea/projects/arabidopsis/jhanson/arabidopsis-nutrition-TOR/analysis_Tom/library-size-normalized_variance-stabilized_data_nutrition.csv")5 Multivariate analysis
5.1 PCA
Principal Component Analysis on the normalized data * Establishment of the PCA
conditions1 <- factor(paste(samples$AZD,samples$Nutrition,sep="_"))
pc <- prcomp(t(vsd))
percent <- round(summary(pc)$importance[2,]*100);percent## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16
## 46 21 14 6 3 2 1 1 1 0 0 0 0 0 0 0
## PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## PC49 PC50 PC51 PC52 PC53 PC54 PC55 PC56
## 0 0 0 0 0 0 0 0- Graphical representation of PC1 x PC2
conds <- droplevels(conditions1)
plot(pc$x[,1],
pc$x[,2],
xlab=paste("Comp. 1 (",percent[1],"%)",sep=""),
ylab=paste("Comp. 2 (",percent[2],"%)",sep=""),
col=pal[as.integer(conds)],
pch=c(15,16,17)[as.factor(samples$Timepoint)],
main="All timepoints")
legend("bottomright",pch=19,
col=pal[1:nlevels(conds)],
legend=levels(conds))
legend("topleft",pch=c(15,16,17),
col="black",
legend=c("T0","T6","T24"))
- Graphical representation of PC2 x PC3
plot(pc$x[,2],
pc$x[,3],
xlab=paste("Comp. 2 (",percent[2],"%)",sep=""),
ylab=paste("Comp. 3 (",percent[3],"%)",sep=""),
col=pal[as.integer(conds)],
pch=c(15,16,17)[as.factor(samples$Timepoint)],
main="All timepoints")
legend("topleft",pch=19,
col=pal[1:nlevels(conds)],
legend=levels(conds))
legend("bottomleft",pch=c(15,16,17),
col="black",
legend=c("T0","T6","T24"))
- Graphical representation of PC1 x PC3
plot(pc$x[,1],
pc$x[,3],
xlab=paste("Comp. 1 (",percent[1],"%)",sep=""),
ylab=paste("Comp. 3 (",percent[3],"%)",sep=""),
col=pal[as.integer(conds)],
pch=c(15,16,17)[as.factor(samples$Timepoint)],
main="All timepoints")
legend("bottomleft",pch=19,
col=pal[1:nlevels(conds)],
legend=levels(conds))
legend("bottomright",pch=c(15,16,17),
col="black",
legend=c("T0","T6","T24"))
6 Differential expression based on the nutrition and treatment at T6
6.1 Filtration of samples based on timepoint
sel <- samples$Timepoint %in% c("T6")
suppressMessages(dds <- DESeqDataSetFromMatrix(
countData = kg[,sel],
colData = samples[sel,],
design = ~ Nutrition * AZD))6.2 Differential expression analysis
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing6.3 Variance Stabilising Transformation
6.3.1 Perform a Variance Stabilizing Transformation for plotting
vst <- varianceStabilizingTransformation(dds,blind=FALSE)
vsd <- assay(vst)
vsd <- vsd - min(vsd)6.4 Contrasts to NPS_DMSO
The contrast by default is the first one (not Intercept)
resultsNames(dds)## [1] "Intercept" "Nutrition_NP_vs_NPS" "Nutrition_NS_vs_NPS"
## [4] "Nutrition_PKS_vs_NPS" "AZD_AZD_vs_DMSO" "NutritionNP.AZDAZD"
## [7] "NutritionNS.AZDAZD" "NutritionPKS.AZDAZD"6.4.1 Nutrition effect of carbon starvation
6.4.1.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_NP_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
SucEffect6hrs <- rownames(res[cutoffs,])
SucLow6hrs <- rownames(res[cutoff2,])
SucHigh6hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 510 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 233 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 277 genes that are repressed6.4.1.2 MA plot
DESeq2::plotMA(res)
6.4.1.3 Volcano plot
volcanoPlot(res, lfc = 1)
6.4.1.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
6.4.2 Nutrition effect of phosphorus starvation
6.4.2.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_NS_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
PiEffect6hrs <- rownames(res[cutoffs,])
PiLow6hrs <- rownames(res[cutoff2,])
PiHigh6hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 3284 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 1246 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 2038 genes that are repressed6.4.2.2 MA plot
DESeq2::plotMA(res)
6.4.2.3 Volcano plot
volcanoPlot(res, lfc=1)
6.4.2.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
6.4.3 Nutrition effect of nitrogen starvation
6.4.3.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_PKS_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
NitEffect6hrs <- rownames(res[cutoffs,])
NitLow6hrs <- rownames(res[cutoff2,])
NitHigh6hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 290 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 162 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 128 genes that are repressed6.4.3.2 MA plot
DESeq2::plotMA(res)
6.4.3.3 Volcano plot
volcanoPlot(res, lfc=1)
6.4.3.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
6.4.4 Nutrition effect of AZD-8055
6.4.4.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "AZD_AZD_vs_DMSO")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
AzdEffect6hrs <- rownames(res[cutoffs,])
AzdLow6hrs <- rownames(res[cutoff2,])
AzdHigh6hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 2564 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 871 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 1693 genes that are repressed6.4.4.2 MA plot
DESeq2::plotMA(res)
6.4.4.3 Volcano plot
volcanoPlot(res, lfc=1)
6.4.4.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
7 Differential expression based on the nutrition and treatment at T24
7.1 Filtration of samples based on timepoint
sel <- samples$Timepoint %in% c("T24")
suppressMessages(dds <- DESeqDataSetFromMatrix(
countData = kg[,sel],
colData = samples[sel,],
design = ~ Nutrition * AZD))7.2 Differential expression analysis
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing7.3 Variance Stabilising Transformation
7.3.1 Perform a Variance Stabilizing Transformation for plotting
vst <- varianceStabilizingTransformation(dds,blind=FALSE)
vsd <- assay(vst)
vsd <- vsd - min(vsd)7.4 Contrasts to NPS_DMSO
The contrast by default is the first one (not Intercept)
resultsNames(dds)## [1] "Intercept" "Nutrition_NP_vs_NPS" "Nutrition_NS_vs_NPS"
## [4] "Nutrition_PKS_vs_NPS" "AZD_AZD_vs_DMSO" "NutritionNP.AZDAZD"
## [7] "NutritionNS.AZDAZD" "NutritionPKS.AZDAZD"7.4.1 Nutrition effect of carbon starvation
7.4.1.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_NP_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
SucEffect24hrs <- rownames(res[cutoffs,])
SucLow24hrs <- rownames(res[cutoff2,])
SucHigh24hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 718 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 503 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 215 genes that are repressed7.4.1.2 MA plot
DESeq2::plotMA(res)
7.4.1.3 Volcano plot
volcanoPlot(res, lfc=1)
7.4.1.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
7.4.2 Nutrition effect of phosphorus starvation
7.4.2.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_NS_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
PiEffect24hrs <- rownames(res[cutoffs,])
PiLow24hrs <- rownames(res[cutoff2,])
PiHigh24hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 4124 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 2167 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 1957 genes that are repressed7.4.2.2 MA plot
DESeq2::plotMA(res)
7.4.2.3 Volcano plot
volcanoPlot(res, lfc = 1)
7.4.2.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
7.4.3 Nutrition effect of nitrogen starvation
7.4.3.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "Nutrition_PKS_vs_NPS")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
NitEffect24hrs <- rownames(res[cutoffs,])
NitLow24hrs <- rownames(res[cutoff2,])
NitHigh24hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 123 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 53 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 70 genes that are repressed7.4.3.2 MA plot
DESeq2::plotMA(res)
7.4.3.3 Volcano plot
volcanoPlot(res, lfc=1)
7.4.3.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
7.4.4 Nutrition effect of AZD-8055
7.4.4.1 Extraction of the results from the DESeq analysis
res <- results(dds,name = "AZD_AZD_vs_DMSO")
cutoffs <- abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff1 <- res$log2FoldChange >= lfc & ! is.na(res$padj) & res$padj <= FDR
cutoff2 <- res$log2FoldChange <= -lfc & ! is.na(res$padj) & res$padj <= FDR
AzdEffect24hrs <- rownames(res[cutoffs,])
AzdLow24hrs <- rownames(res[cutoff2,])
AzdHigh24hrs <- rownames(res[cutoff1,])
message(sprintf("There are %s genes that are differentially expressed",sum(cutoffs)))## There are 5747 genes that are differentially expressedmessage(sprintf("There are %s genes that are induced",sum(cutoff1)))## There are 2876 genes that are inducedmessage(sprintf("There are %s genes that are repressed",sum(cutoff2)))## There are 2871 genes that are repressed7.4.4.2 MA plot
DESeq2::plotMA(res)
7.4.4.3 Volcano plot
volcanoPlot(res, lfc=1)
7.4.4.4 Heatmap
hm <- heatmap.2(t(scale(t(vsd[cutoffs,]))),col=hpal,
Colv=FALSE,dendrogram = "row",trace = "none",labRow = FALSE,
distfun = pearson.dist, labCol = paste(samples$Nutrition,samples$AZD,sep="-")[sel],
hclustfun = function(X){hclust(X,method="ward.D2")},margins = c(6,5))
8 Comparisons of GOI lists (Venn diagrams)
8.1 GOI at T6
- All GOI
grid.draw(venn.diagram(list(AzdEffect6hrs, PiEffect6hrs, SucEffect6hrs, NitEffect6hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
- Downregulated genes
grid.draw(venn.diagram(list(AzdLow6hrs, PiLow6hrs, SucLow6hrs, NitLow6hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
- Upregulated genes
grid.draw(venn.diagram(list(AzdHigh6hrs, PiHigh6hrs, SucHigh6hrs, NitHigh6hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
8.2 GOI at T24
- All GOI
grid.draw(venn.diagram(list(AzdEffect24hrs, PiEffect24hrs, SucEffect24hrs, NitEffect24hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
- Downregulated genes
grid.draw(venn.diagram(list(AzdLow24hrs, PiLow24hrs, SucLow24hrs, NitLow24hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
- Upregulated genes
grid.draw(venn.diagram(list(AzdHigh24hrs, PiHigh24hrs, SucHigh24hrs, NitHigh24hrs),
filename=NULL,
col=pal[1:4],
category.names=c("AZD","Phos. Starv.","Suc. Starv.", "Nit. Starv.")))
8.3 Between timepoints
8.3.1 In response to AZD treatment
- Upregulated
grid.draw(venn.diagram(list(AzdHigh6hrs, AzdHigh24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
- DOwnregulated
grid.draw(venn.diagram(list(AzdLow6hrs, AzdLow24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
8.3.2 In response to Phosphate starvation
- Upregulated
grid.draw(venn.diagram(list(PiHigh6hrs, PiHigh24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
- DOwnregulated
grid.draw(venn.diagram(list(PiLow6hrs, PiLow24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
8.3.3 In response to Sucrose starvation
- Upregulated
grid.draw(venn.diagram(list(SucHigh6hrs, SucHigh24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
- DOwnregulated
grid.draw(venn.diagram(list(SucLow6hrs, SucLow24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
8.3.4 In response to Nitrogen starvation
- Upregulated
grid.draw(venn.diagram(list(NitHigh6hrs, NitHigh24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
- DOwnregulated
grid.draw(venn.diagram(list(NitLow6hrs, NitLow24hrs),
filename=NULL,
col=pal[1:2],
category.names=c("6hrs","24hrs")))
9 Export list of DEGs
write.table(AzdHigh24hrs, file = "AzdHigh24hrs.txt", sep = "\t", row.names = FALSE)
write.table(AzdHigh6hrs, file = "AzdHigh6hrs.txt", sep = "\t", row.names = FALSE)
write.table(PiHigh24hrs, file = "PiHigh24hrs.txt", sep = "\t", row.names = FALSE)
write.table(PiHigh6hrs, file = "PiHigh6hrs.txt", sep = "\t", row.names = FALSE)
write.table(SucHigh24hrs, file = "SucHigh24hrs.txt", sep = "\t", row.names = FALSE)
write.table(SucHigh6hrs, file = "SucHigh6hrs.txt", sep = "\t", row.names = FALSE)
write.table(AzdLow24hrs, file = "AzdLow24hrs.txt", sep = "\t", row.names = FALSE)
write.table(AzdLow6hrs, file = "AzdLow6hrs.txt", sep = "\t", row.names = FALSE)
write.table(PiLow24hrs, file = "PiLow24hrs.txt", sep = "\t", row.names = FALSE)
write.table(PiLow6hrs, file = "PiLow6hrs.txt", sep = "\t", row.names = FALSE)
write.table(SucLow24hrs, file = "SucLow24hrs.txt", sep = "\t", row.names = FALSE)
write.table(SucLow6hrs, file = "SucLow6hrs.txt", sep = "\t", row.names = FALSE)10 Session Info
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] vsn_3.54.0 VennDiagram_1.6.20
## [3] futile.logger_1.4.3 tximport_1.14.0
## [5] forcats_0.4.0 stringr_1.4.0
## [7] dplyr_0.8.3 purrr_0.3.3
## [9] readr_1.3.1 tidyr_1.0.0
## [11] tibble_2.1.3 tidyverse_1.3.0
## [13] scatterplot3d_0.3-41 RColorBrewer_1.1-2
## [15] plotly_4.9.1 pander_0.6.3
## [17] magrittr_1.5 LSD_4.0-0
## [19] limma_3.42.0 hyperSpec_0.99-20180627
## [21] ggplot2_3.2.1 lattice_0.20-38
## [23] here_0.1 gplots_3.0.1.1
## [25] DESeq2_1.26.0 SummarizedExperiment_1.16.0
## [27] DelayedArray_0.12.0 BiocParallel_1.20.0
## [29] matrixStats_0.55.0 Biobase_2.46.0
## [31] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0
## [33] IRanges_2.20.1 S4Vectors_0.24.0
## [35] BiocGenerics_0.32.0 data.table_1.12.6
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 rprojroot_1.3-2 htmlTable_1.13.2
## [4] XVector_0.26.0 fs_1.3.1 base64enc_0.1-3
## [7] rstudioapi_0.10 affyio_1.56.0 bit64_0.9-7
## [10] AnnotationDbi_1.48.0 lubridate_1.7.4 xml2_1.2.2
## [13] splines_3.6.1 geneplotter_1.64.0 knitr_1.26
## [16] zeallot_0.1.0 Formula_1.2-3 jsonlite_1.6
## [19] broom_0.5.2 annotate_1.64.0 cluster_2.1.0
## [22] dbplyr_1.4.2 BiocManager_1.30.10 compiler_3.6.1
## [25] httr_1.4.1 backports_1.1.5 assertthat_0.2.1
## [28] Matrix_1.2-18 lazyeval_0.2.2 cli_1.1.0
## [31] formatR_1.7 acepack_1.4.1 htmltools_0.4.0
## [34] tools_3.6.1 affy_1.64.0 gtable_0.3.0
## [37] glue_1.3.1 GenomeInfoDbData_1.2.2 Rcpp_1.0.3
## [40] cellranger_1.1.0 vctrs_0.2.0 preprocessCore_1.48.0
## [43] gdata_2.18.0 nlme_3.1-142 xfun_0.11
## [46] rvest_0.3.5 testthat_2.3.0 lifecycle_0.1.0
## [49] gtools_3.8.1 XML_3.98-1.20 zlibbioc_1.32.0
## [52] scales_1.1.0 hms_0.5.2 lambda.r_1.2.4
## [55] yaml_2.2.0 memoise_1.1.0 gridExtra_2.3
## [58] rpart_4.1-15 latticeExtra_0.6-28 stringi_1.4.3
## [61] RSQLite_2.1.2 highr_0.8 genefilter_1.68.0
## [64] checkmate_1.9.4 caTools_1.17.1.2 rlang_0.4.2
## [67] pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14
## [70] htmlwidgets_1.5.1 bit_1.1-14 tidyselect_0.2.5
## [73] R6_2.4.1 generics_0.0.2 Hmisc_4.3-0
## [76] DBI_1.0.0 pillar_1.4.2 haven_2.2.0
## [79] foreign_0.8-72 withr_2.1.2 survival_3.1-7
## [82] RCurl_1.95-4.12 nnet_7.3-12 modelr_0.1.5
## [85] crayon_1.3.4 futile.options_1.0.1 KernSmooth_2.23-16
## [88] rmarkdown_1.18 locfit_1.5-9.1 readxl_1.3.1
## [91] blob_1.2.0 reprex_0.3.0 digest_0.6.23
## [94] xtable_1.8-4 munsell_0.5.0 viridisLite_0.3.0